Commonality Feature Representation Learning for Unsupervised Multimodal Change Detection

The main challenge of multimodal change detection (MCD) is that multimodal bitemporal images (MBIs) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL) and constructs a CFRL-based unsupervised MCD f...

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Veröffentlicht in:IEEE transactions on image processing Jg. 34; S. 1219 - 1233
Hauptverfasser: Liu, Tongfei, Zhang, Mingyang, Gong, Maoguo, Zhang, Qingfu, Jiang, Fenlong, Zheng, Hanhong, Lu, Di
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Autoencoders
Change detection
Clustering
Clustering algorithms
Coders
Commonality
commonality feature
Data mining
Decoders
Decoding
Electronic mail
Feature extraction
heterogeneous images
Image reconstruction
Image sensors
Learning
Multimodal change detection
Representation learning
Representations
Training
unsupervised change detection
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:The main challenge of multimodal change detection (MCD) is that multimodal bitemporal images (MBIs) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL) and constructs a CFRL-based unsupervised MCD framework. The CFRL is composed of a Siamese-based encoder and two decoders. First, the Siamese-based encoder can map original MBIs in the same feature space for extracting the representative features of each modality. Then, the two decoders are used to reconstruct the original MBIs by regressing themselves, respectively. Meanwhile, we swap the decoders to reconstruct the pseudo-MBIs to conduct modality alignment. Subsequently, all reconstructed images are input to the Siamese-based encoder again to map them in a same feature space, by which representative features are obtained. On this basis, latent commonality features between MBIs can be extracted by minimizing the distance between these representative features. These latent commonality features are comparable and can be used to identify changes. Notably, the proposed CFRL can be performed simultaneously in two modalities corresponding to MBIs. Therefore, two change magnitude images (CMIs) can be generated simultaneously by measuring the difference between the commonality features of MBIs. Finally, a simple threshold algorithm or a clustering algorithm can be employed to divide CMIs into binary change maps. Extensive experiments on six publicly available MCD datasets show that the proposed CFRL-based framework can achieve superior performance compared with other state-of-the-art approaches.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2025.3539461